---
license: cc-by-nc-4.0
language:
- en
pretty_name: PanoCaps
task_categories:
- image-segmentation
- image-to-text
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: split
    dtype: string
  - name: image_id
    dtype: string
  - name: file_name
    dtype: string
  - name: image_info
    struct:
    - name: data_source
      dtype: string
    - name: file_name
      dtype: string
    - name: height
      dtype: int64
    - name: id
      dtype: string
    - name: width
      dtype: int64
  - name: caption_info
    struct:
    - name: caption
      dtype: string
    - name: caption_ann
      dtype: string
    - name: id
      dtype: int64
    - name: image_id
      dtype: string
    - name: label_matched
      list:
      - name: mask_ids
        sequence: int64
      - name: txt_desc
        dtype: string
    - name: labels
      sequence: string
  - name: mask_annotations
    list:
    - name: area
      dtype: int64
    - name: bbox
      sequence: float64
    - name: category_id
      dtype: int64
    - name: id
      dtype: int64
    - name: image_id
      dtype: string
    - name: iscrowd
      dtype: int64
    - name: segmentation
      struct:
      - name: counts
        dtype: string
      - name: size
        sequence: int64
    - name: thing_or_stuff
      dtype: string
  - name: categories
    list:
    - name: id
      dtype: int64
    - name: name
      dtype: string
  splits:
  - name: train
    num_bytes: 29443350
    num_examples: 2070
  - name: val
    num_bytes: 4782919
    num_examples: 420
  - name: test
    num_bytes: 10976834
    num_examples: 980
  download_size: 25273455
  dataset_size: 45203103
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
---
# PanoCaps: A Human-Annotated Benchmark for Panoptic Grounded Captioning

[![Paper](https://img.shields.io/badge/ArXiv-Paper-brown)](https://arxiv.org/abs/2609.19143)
[![Code](https://img.shields.io/badge/GitHub-Link-orange)](https://github.com/sarapieri/panorama_grounding)
[![Project](https://img.shields.io/badge/HuggingFace-Project-blue)](https://huggingface.co/Panorama-grounding)
[![Website](https://img.shields.io/badge/Web-Page-purple)](https://www.di.ens.fr/willow/research/panorama/)

<p align="center">
  <img src="https://huggingface.co/datasets/Panorama-grounding/PanoCaps/resolve/main/panocaps_card.png"
       width="100%"
       alt="PanoCaps example: grounded masks, grounded phrases and the grounded caption" />
</p>

PanoCaps is a benchmark for **panoptic grounded captioning**: a model writes a full-scene caption and grounds every mentioned entity, things and stuff alike, to pixel-level masks.

It contains **3,470 images** and **34K panoptic regions**, averaging **~9 grounded entities per image**, with **>99% of regions grounded**. Captions are human-written and verified, cover the entire visible scene, use open-vocabulary descriptions that go beyond category labels, and support **one-to-many** and **many-to-one** text ↔ mask mappings. Masks follow COCO-style panoptic annotations.

For research and non-commercial use only.

See also our cleaned version of the COCONut-PanCap training captions,
[COCONut-PanCap Re-captioned](https://huggingface.co/datasets/Panorama-grounding/COCONut-PanCap-Recaptioned).

## Images

Images are **not** included in this repository. Download them from the source datasets below, or get **both images and annotations, already formatted**, as a single archive from this [Google Drive link](https://drive.google.com/file/d/1iNEuKWJdv1wKKGycP-4hTKHdfmcuQEOB/view?usp=sharing). The annotations reference the images by `file_name` and `id`.

| Dataset | Data Download Link                          | Associated Publication |
|---------|---------------------------------------------|-----------------------------------------|
| ADE20K  | [ADE20K Download](https://groups.csail.mit.edu/vision/datasets/ADE20K/) | [ADE20K Paper](https://arxiv.org/abs/1608.05442)                 |
| COCONut | [COCONut GitHub](https://github.com/bytedance/coconut_cvpr2024) | [COCONut Paper](https://arxiv.org/abs/2404.08639)                        |
| VIPSeg  | [VIPSeg GitHub](https://github.com/VIPSeg-Dataset/VIPSeg-Dataset/) | [VIPSeg Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Miao_Large-Scale_Video_Panoptic_Segmentation_in_the_Wild_A_Benchmark_CVPR_2022_paper.html)                      |

## Download

```bash
# annotations only (load_dataset reads the parquet in data/, not these)
hf download Panorama-grounding/PanoCaps --repo-type dataset --include "annotations/*" \
    --local-dir /path/to/data/PanoCaps

# or images + annotations, pre-formatted, from the Google Drive archive above
pip install gdown && gdown 1iNEuKWJdv1wKKGycP-4hTKHdfmcuQEOB -O PanoCaps.zip
unzip PanoCaps.zip -d /path/to/data/
```

## Formats

The same annotations are published in two formats:

- **`annotations/`** — COCO-style JSON, as `<split>_caption.json` / `<split>_mask.json` pairs for `train`, `val` and `test`. Recommended for **training and evaluation**. Join the two files on `image_id` / `id`.
- **`data/`** — parquet, merging caption and mask information into one entry per image. Recommended for **browsing in the Dataset Viewer**.

Note that `load_dataset` reads only `data/`; the JSON annotations have to be downloaded explicitly.

### Caption files

`annotations[*]`:

| Field | Description |
|---|---|
| `caption` | Plain natural-language caption. |
| `caption_ann` | The same caption with inline `<mask_ids:text>` grounding tags, e.g. `a <5,6,7:row of buses>`. |
| `label_matched` | List of `{"mask_ids": [...], "txt_desc": "..."}`, mapping each grounded span to one or more masks. |
| `id` | Caption annotation id. |
| `image_id` | Matches `images[*].id` and the mask file. |
| `labels` | Semantic label of every mask of the image, in mask order (so `len(labels)` equals the number of masks, and labels may repeat). |

**`mask_ids` are positional indices** into that image's list of masks — equivalently, into `labels` — not the global `id` values used in the mask file. The same mask may be referenced by several spans, for instance when an entity is mentioned again later in the caption.

### Mask files

`annotations[*]`:

| Field | Description |
|---|---|
| `id` | Region id, unique within the split. |
| `image_id` | Links the region to its image and caption. |
| `segmentation` | `{"size": [height, width], "counts": ...}`, COCO panoptic RLE. |
| `area` | Region area in pixels. |
| `bbox` | COCO-format box, `[x, y, width, height]`. |
| `thing_or_stuff` | `thing` for object-like regions, `stuff` for background. |

Both caption and mask files also carry `images[*]` with `file_name`, `height`, `width`, `id` and `data_source`.

## Annotation

Images were curated from ADE20K, COCONut and VIPSeg for visual quality and suitability for dense grounding. Paid professional annotators then wrote long-form scene descriptions covering attributes, relationships and all visible entities, tagged every textual reference with `<mask_ids:description>` markers, and produced the corresponding `label_matched` mapping. A separate quality-control pass verified grounding ids, completeness of region coverage and annotation consistency.

The base panoptic masks come from the source datasets; all captions and grounding annotations were created specifically for PanoCaps.

## License

This repository merges, normalizes and redistributes content from existing datasets, and is therefore released **strictly for research and non-commercial use**. Commercial use is not permitted, and users must comply with the licenses of each source dataset.

## Citation

If you find our work useful for your research, please consider citing our paper:

```bibtex
@article{pieri2026panorama,
  title   = {PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection},
  author  = {Pieri, Sara and Kazakos, Evangelos and Chen, Shizhe and Sivic, Josef and Schmid, Cordelia},
  journal = {arXiv preprint arXiv:2609.19143},
  year    = {2026}
}
```